Predictions of Root Tensile Strength for Different Vegetation Species Using Individual and Ensemble Machine Learning Models

Semwal, Tarun; Priyanka, None; Kumar, Praveen; Dutt, Varun; Uday, K. V.

doi:10.1007/978-3-031-20241-4_7

Cited by 1 publication

(3 citation statements)

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“…This study aims to bridge this knowledge gap by examining different approaches to tackle class imbalance and exploring diverse ML models to improve the prediction of soil movement. Various multivariate classification models, including random forest (RF); adaptive boosting (AdaBoost); extreme gradient boosting (XG-Boost); light gradient boosting machine (LightGBM); category boosting (CatBoost); long short-term memory (LSTM); multilayer perceptron (MLP); and an ensemble of RF, Ad-aBoost, XGBoost, LightGBM, and CatBoost, are developed to predict soil movement when coupled with class imbalance techniques Semwal et al, 2022;Wu et al, 2020;Pathania et al, 2021;Zhang et al, 2022;Sahin, 2022;Kumar et al, 2020;Kumar et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers developed various ML models to predict soil movement and prediction problems in other fields Semwal et al, 2022;Wu et al, 2020;Pathania et al, 2021;Zhang et al, 2022;Sahin, 2022;Kumar et al, 2020). For example, developed an ensemble of ML models (RF, bagging, stacking, and voting) for predicting soil movement at the Tangni landslide in Uttarakhand, India.…”

Section: Introductionmentioning

confidence: 99%

“…The results indicate that the ensemble models outperformed the SMO and AR models in predicting soil movement. Furthermore, Semwal et al (2022) developed sequential minimal optimization regression (SMOreg), instance-based learning (IBk), RF, linear regression (LR), MLP, and ensemble ML models to predict root tensile strength for different vegetation species. The results show that the MLP performed better than the other models, providing more accurate predictions of root tensile strength.…”

Section: Introductionmentioning

confidence: 99%

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Addressing class imbalance in soil movement predictions

Kumar,

Priyanka,

Uday

et al. 2024

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat. Accurately predicting soil movement from monitoring data is challenging due to its complexity and inherent class imbalance. This study proposes developing machine learning (ML) models with oversampling techniques to address the class imbalance issue and develop a robust soil movement prediction system. The dataset, comprising 2 years (2019–2021) of monitoring data from a landslide in Uttarakhand, has a 70:30 ratio of training and testing data. To tackle the class imbalance problem, various oversampling techniques, including the synthetic minority oversampling technique (SMOTE), K-means SMOTE, borderline-SMOTE, and adaptive SMOTE (ADASYN), were applied to the training dataset. Several ML models, namely random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), category boosting (CatBoost), long short-term memory (LSTM), multilayer perceptron (MLP), and a dynamic ensemble, were trained and compared for soil movement prediction. A 5-fold cross-validation method was applied to optimize the ML models on the training data, and the models were tested on the testing set. Among these ML models, the dynamic ensemble model with K-means SMOTE performed the best in testing, with an accuracy, precision, and recall rate of 0.995, 0.995, and 0.995, respectively, and an F1 score of 0.995. Additionally, models without oversampling exhibited poor performance in training and testing, highlighting the importance of incorporating oversampling techniques to enhance predictive capabilities.

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